Fuzzy Time Series Singh Method for Forecasting Tourist Arrivals at Kinantan Wildlife and Cultural Park Bukittinggi - Olivin Adelia Huqmi; Fadhilah Fitri; Tessy Octavia Mukhti (UNP Journal of Statistics and Data Science, 2026)
Tourism is a key sector in regional development, contributing to economic growth, job creation, and cultural preservation. In Bukittinggi, West Sumatra, the Kinantan Wildlife and Cultural Park (TMSBK) is a major tourist destination, known for its historical and educational value. Tourist visits to TMSBK show fluctuating trends influenced by seasonal factors, socio-economic conditions, and national or global events. These dynamics make accurate forecasting essential for effective tourism planning and management. This study aims to forecast monthly tourist visits to TMSBK using the Fuzzy Time Series (FTS) Singh method, which is suitable for uncertain and fluctuating time series data. The research used historical visitor data from 2021 to 2024 obtained from the Central Bureau of Statistics. The forecasting process included defining the universe of discourse, forming class intervals, fuzzifying historical data, establishing fuzzy logical relationships (FLR), and generating forecasts. The accuracy of the forecasts was measured using Mean Absolute Percentage Error (MAPE), with a result of 19.8%, indicating good predictive performance. The results show that the FTS Singh method successfully follows the fluctuation pattern of actual visitor data. This method provides valuable insights for destination managers in planning operations, promotional efforts, and service improvements. Therefore, the FTS Singh method can be considered a reliable tool to support sustainable tourism development and decision-making in Bukittinggi.